7 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs
Shirly Stephen, Mitchell Faulk, Krzysztof Janowicz +10
Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open K…
The KnowWhereGraph Ontology
Cogan Shimizu, Shirly Stephe, Adrita Barua +21
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate…
A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space
Zhangyu Wang, Lantian Xu, Zhifeng Kong +3
Hyperbolic embeddings are a class of representation learning methods that offer competitive performances when data can be abstracted as a tree-like graph. However, in practice, lea…
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz +1
A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric c…
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
Nemin Wu, Qian Cao, Zhangyu Wang +12
Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, network…
Probing the Information Theoretical Roots of Spatial Dependence Measures
Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai +1
Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial da…